Digital subscriber line (DSL) technologies generally include digital subscriber line equipment and services using packet-based architectures, such as, for example, Asymmetric DSL (ADSL), High-speed DSL (HDSL), Symmetric DSL (SDSL), and/or Very high-speed/Very high-bit-rate DSL (VDSL). Such DSL technologies can provide extremely high bandwidth over a twisted pair line and offers great potential for bandwidth-intensive applications. DSL services in the 30 K-30 MHz band are however more dependent on line conditions (for example, the length, quality and environment of the line) than is Plain Old Telephone Service (POTS) operating in the <4K band.
While some lines (loops) are in good physical condition for implementing DSL (for example, having short to moderate lengths with operative micro-filters or splitters correctly installed and with no bridged taps and no bad splices), many lines are not as suitable. For example, micro-filters may be missing or inoperative. Even where a line is initially qualified at a given quality, issues may arise over time such that long-term line management is needed.
Assessment of a line's physical configuration is an important step in the implementation and management of any DSL network. Physical line diagnostics includes a detection and/or localization of conditions or faults on a line. Such physical line diagnostics are important because the bit-rate that can be achieved for a given communication technology (e.g., DSL) is often dependent on the physical configuration of the line.
In addition to a given line's physical configuration, impulse noise or other noise may also limit the performance of DSL systems dynamically. Such noise may originate from sources that may include other DSL systems, or other systems whose signals are coupled on the twisted pairs used by a DSL system. Noise may be particularly strong when twisted pairs are physically close, such as when they share a common binder. Knowledge of noise effects is very useful for DSL management operations, because it helps with identifying the cause of poor performance, and because it may become a basis for correcting the problem, for example with an appropriate filter.
Line diagnostics in the art generally rely on analysis of data parameters collected from a line to estimate a line configuration or detect the presence of a fault in the line, such as a missing micro-filter. Conventional analysis techniques that attempt to correlate a particular value of a parameter (e.g., transmit power), or a change in that value, to a particular fault are subject to misdetection resulting from either a first type of error where the line data analysis algorithm has a sensitivity to real features that is too low, or a second type of error where sensitivity to spurious features is too high.
Techniques improving detection capability and/or improving the accuracy of automated line diagnostics are therefore advantageous.